Reliable landslide mapping in active mining regions is constrained by two coupled issues: severe domain shift from public datasets and extremely limited local annotations. In line with Transformer-centric intelligent interpretation of complex remote-sensing scenes, this study proposes a label-efficient transfer segmentation framework from a public source corpus to target open-pit mines built on SegFormer with a lightweight hybrid adapter that couples global context modeling with mining-specific directional cues. The pipeline combines source-domain Transformer pre-training, class-conditional feature alignment, Bayesian uncertainty estimation, and human-guided active learning. First, the backbone is pre-trained on the GDCLD source domain to learn transferable landslide morphology priors. Second, a joint optimization stage with class-conditional alignment reduces source and target embedding discrepancy during adaptation. Third, Monte Carlo dropout is enabled at inference to estimate predictive distributions, and sample acquisition is driven by mutual-information-based querying to prioritize epistemically informative target patches, addressing the small-sample supervision challenge emphasized in remote-sensing deep learning. This design turns uncertainty into an operational annotation policy rather than a passive diagnostic output. Experimental results show that the framework consistently outperforms deterministic counterparts and strong active-learning baselines in spectrally complex mine scenes, while approaching the fully supervised upper bound with only a small fraction of local labels. The approach is especially effective in shadowed benches and fault-adjacent slopes, supporting trustworthy deployment for geohazard monitoring and disaster-relevant slope safety workflows; extension to multi-modal constraints (e.g., SAR or elevation) is discussed as future work.
Li et al. (Mon,) studied this question.